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https://web.archive.org/web/20220302023647/http:/news.mit.edu/2022/artificial-intelligence-anomalies-data-0225

Using artificial intelligence to find anomalies hiding in massive datasets

Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train.



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Using artificial intelligence to find anomalies hiding in massive datasets

https://web.archive.org/web/20220302023647/http:/news.mit.edu/2022/artificial-intelligence-anomalies-data-0225

Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train.



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https://web.archive.org/web/20220302023647/http:/news.mit.edu/2022/artificial-intelligence-anomalies-data-0225

Using artificial intelligence to find anomalies hiding in massive datasets

Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train.

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      Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train.
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      Jie Chen, MIT-IBM Watson AI lab, Normalizing flow models

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      Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train.
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